As the Internet matures and extends its reach, more and more products previously available only in physical stores are being offered for sale online. In recent years, the number of online pharmacies has grown tremendously, but with increased convenience and competition come new and unforeseen risks. Licit online pharmacies provide a safe and potentially cost-saving alternative to local stores, allowing patients to shop around for deals in a market known for sometimes extortionate pricing. The increasing acceptance of purchasing prescription drugs online has also opened the doors for illicit players to enter the market, offering unfettered access to prescription drugs and even controlled substances. Such sites not only threaten the rest of the emerging industry, but also pose dramatic risks to drug supply chain integrity and public health.
Current attempts to safeguard customers take the form of white- and black-lists, painstakingly assembled and maintained by third parties such as the National Association Board of Pharmacies (NABP) or LegitScript. These present two problems. First, a white list verification system requires consumer awareness of the problem, and for consumers to take the extra step of checking a potential pharmacy against known lists. Second, the dynamic nature of online commerce makes it trivial to shut a site down once it has been blacklisted and start it up again under a new domain. Though a white list might be feasibly maintained given a high level of effort, the creation and upkeep of a comprehensive blacklist of online pharmacies is impractical.
One example of an existing system for digital classification of licit online pharmacies (LOPs) and illicit online pharmacies (IOPs) is the work by Corona, et al (2015). Corona aims to build a database of online pharmacies using textual content analysis, i.e., analyzing the HTML or other content appearing on the pharmacy website itself to determine whether the online pharmacy is illicit or licit. This approach has a major disadvantage in that it can be manipulated by IOPs to affect prediction results. For example, if the prediction is based on certain content appearing on the websites, then IOPs could delete/change the content to confuse the model by making their IOPs look similar to LOPs.
Thus, there is a need in the art for an automated, algorithmic method of determining the legitimacy of an online pharmacy that is based on publicly-available data and that cannot easily be manipulated by TOP operators, in order to more effectively regulate the online sale of pharmaceuticals. The present invention satisfies that need.